where \(T\)  is the last number of iteration and \({\Omega }\)  is the set of the constellations. Note that the use of (REF ) and (REF ) eliminates the trial and error issues for finding optimal learning parameters used in most of Bayesian learning iterative receivers, Eq. (37) and (38) in [1]}, Eqs. (15) and (16) in[2]}, and Eq. (8) in [3]}.
The complete pseudo-code is shown in Alg. REF .

LBL Algorithm
 \(\tilde{{\bf x}}^{(0)}=0,T_{\rm max}=10\) 
(REF ) is not satisfied
1. Compute the mean and variance, \({\mu }^{(t)},{\Sigma }\) , in (REF )
